By refining prediction, methods seek to find a good ordinal ranking of predictions. This may or may not result in a calibrated model as it has not been studied for this problem extensively. [1]} incorporated `Correctness Ranking Loss' to allow a DNN to learn appropriate ordinal rankings for classified samples. They also observed that their approach helped in calibrating the network; however, do not discuss the reasoning behind this observation. As a replacement for confidence estimate, [2]} introduced `TrustScore', which provides a better ordinal ranking of predictions than the output of the network. They utilised the ratio between the distance from the sample to the nearest class different from the predicted class and the distance to the predicted class as the trust score. ConfidNet [3]} incorporates the learning of this trust score as an additional branch in the network. In the post-hoc stage, ConfidNet branch of the classifier is trained to predict a confidence score which mimics the reliability of the network on its prediction.
